XGBoost-B-GHM: An Ensemble Model with Feature Selection and GHM Loss Function Optimization for Credit Scoring

IF 2.3 4区 社会学 Q1 SOCIAL SCIENCES, INTERDISCIPLINARY Systems Pub Date : 2024-07-14 DOI:10.3390/systems12070254
Yuxuan Xia, Shanshan Jiang, Lingyi Meng, Xin Ju
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Abstract

Credit evaluation has always been an important part of the financial field. The existing credit evaluation methods have difficulty in solving the problems of redundant data features and imbalanced samples. In response to the above issues, an ensemble model combining an advanced feature selection algorithm and an optimized loss function is proposed, which can be applied in the field of credit evaluation and improve the risk management ability of financial institutions. Firstly, the Boruta algorithm is embedded for feature selection, which can effectively reduce the data dimension and noise and improve the model’s capacity for generalization by automatically identifying and screening out features that are highly correlated with target variables. Then, the GHM loss function is incorporated into the XGBoost model to tackle the issue of skewed sample distribution, which is common in classification, and further improve the classification and prediction performance of the model. The comparative experiments on four large datasets demonstrate that the proposed method is superior to the existing mainstream methods and can effectively extract features and handle the problem of imbalanced samples.
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XGBoost-B-GHM:针对信用评分的特征选择和 GHM 损失函数优化集合模型
信用评估一直是金融领域的重要组成部分。现有的信用评价方法难以解决数据特征冗余和样本不平衡的问题。针对上述问题,本文提出了一种结合先进特征选择算法和优化损失函数的集合模型,可应用于信用评价领域,提高金融机构的风险管理能力。首先,嵌入 Boruta 算法进行特征选择,通过自动识别和筛选出与目标变量高度相关的特征,有效降低数据维度和噪声,提高模型的泛化能力。然后,在 XGBoost 模型中加入 GHM 损失函数,以解决分类中常见的样本分布偏斜问题,进一步提高模型的分类和预测性能。在四个大型数据集上的对比实验表明,所提出的方法优于现有的主流方法,能有效地提取特征并处理不平衡样本问题。
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来源期刊
Systems
Systems Decision Sciences-Information Systems and Management
CiteScore
2.80
自引率
15.80%
发文量
204
审稿时长
11 weeks
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